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Multi-modal non-prototypical music mood analysis in continuous space: Reliability and performances

  • Technical University of Munich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

Music Mood Classification is frequently turned into 'Music Mood Regression' by using a continuous dimensional model rather than discrete mood classes. In this paper we report on automatic analysis of performances in a mood space spanned by arousal and valence on the 2.6 k songs NTWICM corpus of popular UK chart music in full realism, i. e., by automatic web-based retrieval of lyrics and diverse acoustic features without pre-selection of prototypical cases. We discuss optimal modeling of the gold standard by introducing the evaluator weighted estimator principle, group-wise feature relevance, 'tuning' of the regressor, and compare early and late fusion strategies. In the result, correlation coefficients of .736 (valence) and .601 (arousal) are reached on previously unseen test data.

Original languageEnglish
Title of host publicationProceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011
PublisherInternational Society for Music Information Retrieval
Pages759-764
Number of pages6
ISBN (Print)9780615548654
StatePublished - 2011
Event12th International Society for Music Information Retrieval Conference, ISMIR 2011 - Miami, FL, United States
Duration: 24 Oct 201128 Oct 2011

Publication series

NameProceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011

Conference

Conference12th International Society for Music Information Retrieval Conference, ISMIR 2011
Country/TerritoryUnited States
CityMiami, FL
Period24/10/1128/10/11

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